1.3 Protein structure and function

Proteins are the workhorses of life, carrying out essential functions in cells. Their structure determines their function, from simple enzymes to complex molecular machines. Understanding protein structure is key to unraveling biological processes and developing new therapies.

This topic explores the levels of protein structure, folding mechanisms, and functional domains. We'll examine how proteins interact, methods for predicting and visualizing their structures, and the role of protein structure in disease and drug design.

Levels of protein structure

• Protein structure hierarchy plays a crucial role in bioinformatics, enabling researchers to understand protein function and interactions
• Analyzing protein structure levels aids in predicting protein behavior, designing drugs, and studying evolutionary relationships

Primary structure

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• Linear sequence of amino acids connected by peptide bonds
• Determined by the genetic code and forms the foundation for higher-order structures
• Represented using one-letter or three-letter amino acid codes
• Influences protein folding and ultimate three-dimensional shape
• Alterations in primary structure can lead to significant changes in protein function (sickle cell anemia)

Secondary structure

• Local folding patterns of the polypeptide chain
• Alpha helices form spiral structures stabilized by hydrogen bonds
• Beta sheets consist of extended strands connected by hydrogen bonds
• Turn and loop regions connect different secondary structure elements
• Predicted using algorithms based on amino acid sequence (Chou-Fasman method)

Tertiary structure

• Overall three-dimensional shape of a single polypeptide chain
• Formed by interactions between side chains of amino acids
• Includes domains, which are distinct functional or structural units
• Stabilized by various forces (hydrophobic interactions, salt bridges)
• Determines protein function and binding capabilities

Quaternary structure

• Arrangement of multiple polypeptide chains in a protein complex
• Subunits can be identical or different
• Held together by non-covalent interactions and sometimes disulfide bonds
• Examples include hemoglobin (four subunits) and antibodies (multiple chains)
• Crucial for complex protein functions and regulation

Protein folding mechanisms

• Understanding protein folding is essential for predicting protein structure from sequence data
• Folding mechanisms influence protein stability, function, and potential for misfolding-related diseases

Hydrophobic interactions

• Drive the collapse of protein structure in aqueous environments
• Nonpolar amino acids cluster in the protein core, away from water
• Contribute significantly to the stability of the folded state
• Play a key role in membrane protein folding and stability
• Can be disrupted by denaturants (urea, guanidinium chloride)

Hydrogen bonding

• Forms between hydrogen atoms and electronegative atoms (oxygen, nitrogen)
• Stabilizes secondary structure elements (alpha helices, beta sheets)
• Contributes to the specificity of protein-protein and protein-ligand interactions
• Can occur within the protein or between the protein and surrounding water
• Strength varies depending on the distance and angle between atoms

Disulfide bridges

• Covalent bonds formed between cysteine residues
• Provide additional stability to protein structure
• Common in extracellular and secreted proteins
• Can be reduced and reformed during protein folding and unfolding
• Important for maintaining the structure of many enzymes and hormones (insulin)

Chaperone proteins

• Assist in proper protein folding and prevent aggregation
• Heat shock proteins (HSPs) are a major class of chaperones
• Function in both normal conditions and during cellular stress
• Can unfold and refold misfolded proteins
• Play a role in protein quality control and degradation pathways

Protein domains and motifs

• Critical for understanding protein function and evolution in bioinformatics
• Aid in predicting protein interactions and functional sites

Functional domains

• Distinct regions of a protein with specific biochemical functions
• Can fold independently and often be expressed as separate proteins
• Examples include kinase domains, DNA-binding domains, and transmembrane domains
• Identified through sequence and structure analysis
• Often conserved across different proteins and species

Structural motifs

• Recurring three-dimensional arrangements of secondary structure elements
• Include common patterns (helix-turn-helix, zinc finger, beta-barrel)
• Can be associated with specific functions or binding properties
• Identified using structural alignment and classification tools
• Important for protein structure prediction and design

Conserved sequences

• Amino acid patterns preserved through evolutionary history
• Indicate functionally or structurally important regions
• Include active sites, binding motifs, and regulatory sequences
• Identified through multiple sequence alignments
• Used to infer protein function and evolutionary relationships

Protein function classification

• Essential for organizing and understanding the vast array of proteins in bioinformatics
• Helps in predicting functions of newly discovered proteins

Enzymes

• Catalyze biochemical reactions in cells
• Classified by the type of reaction they catalyze (EC number system)
• Structure includes active sites and sometimes allosteric sites
• Kinetics described by parameters (Km, Vmax, kcat)
• Examples include DNA polymerase, proteases, and kinases

Structural proteins

• Provide mechanical support and maintain cell shape
• Include cytoskeletal proteins (actin, tubulin) and extracellular matrix proteins (collagen)
• Often form fibers or networks
• Can be dynamic and undergo assembly/disassembly
• Play roles in cell movement and division

Transport proteins

• Facilitate movement of molecules across membranes or within cells
• Include ion channels, carrier proteins, and motor proteins
• Often have specific binding sites for their cargo
• Can be active (require energy) or passive transporters
• Examples include glucose transporters and sodium-potassium pumps

Regulatory proteins

• Control cellular processes and gene expression
• Include transcription factors, signal transduction proteins, and hormones
• Often have modular structures with distinct functional domains
• Can undergo post-translational modifications to alter their activity
• Examples include p53 (tumor suppressor) and insulin (metabolic regulator)

Protein-protein interactions

• Central to understanding cellular processes and signaling pathways in bioinformatics
• Critical for predicting protein function and designing therapeutic interventions

Binding sites

• Specific regions on proteins that interact with other molecules
• Can be pockets, clefts, or surface patches
• Often involve complementary shapes and chemical properties
• Characterized by conserved residues and structural features
• Identified through experimental methods and computational predictions

Allosteric regulation

• Modulation of protein activity through binding at a site distant from the active site
• Involves conformational changes that affect protein function
• Can be positive (activation) or negative (inhibition)
• Important in metabolic regulation and signal transduction
• Examples include hemoglobin's oxygen binding and enzyme regulation

Protein complexes

• Stable or transient assemblies of multiple protein subunits
• Perform complex cellular functions (ribosomes, proteasomes)
• Formation often involves hierarchical assembly of subcomplexes
• Studied using techniques (yeast two-hybrid, mass spectrometry)
• Represented in databases (IntAct, STRING) for bioinformatics analysis

Protein structure prediction

• Crucial for understanding protein function when experimental structures are unavailable
• Combines computational methods with experimental data in bioinformatics

Homology modeling

• Predicts 3D structure based on known structures of related proteins
• Requires a template with significant sequence similarity
• Involves sequence alignment, backbone generation, and loop modeling
• Accuracy depends on sequence identity and quality of the template
• Widely used for protein engineering and drug design

Ab initio methods

• Predict structure from sequence alone, without relying on known structures
• Based on physical principles and energy minimization
• Computationally intensive and limited to smaller proteins
• Includes methods (Rosetta, I-TASSER)
• Useful for novel proteins with no known homologs

Machine learning approaches

• Utilize large datasets of known protein structures to predict new ones
• Include deep learning methods (AlphaFold, RoseTTAFold)
• Can incorporate evolutionary information and physical constraints
• Have significantly improved prediction accuracy in recent years
• Revolutionizing structural biology and drug discovery

Experimental methods

• Provide high-resolution structural data essential for bioinformatics analyses
• Each method has strengths and limitations for different types of proteins

X-ray crystallography

• Produces atomic-resolution structures of crystallized proteins
• Involves growing protein crystals and analyzing X-ray diffraction patterns
• Provides detailed information about atom positions and bond lengths
• Challenges include protein crystallization and phase determination
• Has contributed the majority of structures in the Protein Data Bank

NMR spectroscopy

• Determines protein structure in solution
• Provides information about protein dynamics and flexibility
• Based on nuclear magnetic resonance phenomena
• Typically limited to smaller proteins (<30 kDa)
• Useful for studying intrinsically disordered proteins and protein-ligand interactions

Cryo-electron microscopy

• Images frozen protein samples using electron beams
• Can resolve structures of large complexes and membrane proteins
• Does not require protein crystallization
• Recent advances have achieved near-atomic resolution
• Particularly useful for studying macromolecular assemblies and conformational states

Protein structure databases

• Essential resources for storing, accessing, and analyzing protein structural data in bioinformatics
• Facilitate research in structural biology, drug discovery, and protein engineering

Protein Data Bank (PDB)

• Primary repository for experimentally determined 3D structures
• Contains structures from X-ray crystallography, NMR, and cryo-EM
• Provides standardized file formats (PDB, mmCIF) for structure representation
• Includes tools for searching, visualizing, and analyzing structures
• Widely used in structure-based drug design and protein engineering

UniProt

• Comprehensive resource for protein sequence and functional information
• Integrates data from Swiss-Prot, TrEMBL, and PIR databases
• Provides cross-references to other databases, including structural information
• Includes tools for sequence analysis and annotation
• Essential for linking sequence, structure, and function in bioinformatics studies

SCOP and CATH

• Hierarchical classifications of protein structures
• SCOP (Structural Classification of Proteins) organizes proteins by evolutionary relationships
• CATH (Class, Architecture, Topology, Homologous superfamily) classifies proteins by structural similarity
• Both databases provide insights into protein evolution and folding
• Useful for identifying structural motifs and predicting functions of novel proteins

Protein structure visualization

• Critical for interpreting and communicating structural data in bioinformatics
• Enables researchers to explore protein features and interactions visually

PyMOL

• Popular molecular visualization software
• Offers high-quality rendering and publication-ready images
• Provides a Python-based scripting interface for customization
• Supports various molecular representations (cartoon, surface, sticks)
• Includes tools for structural alignment and distance measurements

Chimera

• Extensible program for interactive visualization and analysis
• Offers a wide range of built-in tools for structure manipulation
• Supports multiscale models, from atoms to cellular components
• Provides interfaces to external web services and databases
• Useful for integrating structural data with other types of molecular information

Jmol

• Java-based viewer for chemical structures in 3D
• Can be embedded in web pages for interactive online visualization
• Supports a wide range of chemical file formats
• Provides a scripting language for customization and automation
• Useful for educational purposes and web-based structural biology resources

Protein structure analysis tools

• Essential for extracting meaningful information from protein structures in bioinformatics
• Aid in functional annotation, evolutionary studies, and structure-based design

BLAST for proteins

• Compares protein sequences to databases of known sequences
• Identifies homologous proteins and conserved domains
• Uses scoring matrices (BLOSUM, PAM) to assess sequence similarity
• Provides statistical significance measures (E-value)
• Essential for inferring function and evolutionary relationships

Multiple sequence alignment

• Aligns three or more protein sequences simultaneously
• Identifies conserved residues and motifs across related proteins
• Uses algorithms (ClustalW, MUSCLE, T-Coffee)
• Crucial for phylogenetic analysis and structure prediction
• Helps in identifying functionally important regions in proteins

Structural alignment

• Compares 3D structures of proteins to identify similarities
• Uses algorithms based on geometric and topological features
• Tools include DALI, TM-align, and FATCAT
• Useful for detecting remote homologs and structural motifs
• Aids in understanding protein evolution and function

Protein structure and disease

• Understanding protein structure-function relationships is crucial for disease research and drug development in bioinformatics
• Structural insights can reveal disease mechanisms and guide therapeutic strategies

Misfolding and aggregation

• Occurs when proteins fail to achieve or maintain their native structure
• Can lead to loss of function or toxic gain of function
• Associated with neurodegenerative diseases (Alzheimer's, Parkinson's)
• Studied using techniques (circular dichroism, fluorescence spectroscopy)
• Target for therapeutic interventions (chaperone modulators, aggregation inhibitors)

Conformational diseases

• Result from changes in protein structure or dynamics
• Include prion diseases and some forms of cancer
• Often involve mutations that alter protein stability or interactions
• Studied using structural biology and biophysical methods
• Insights guide development of targeted therapies and diagnostic tools

Structure-based drug design

• Utilizes knowledge of protein structure to develop therapeutic compounds
• Involves computational methods (docking, virtual screening) and experimental validation
• Aims to identify molecules that bind specific protein targets
• Has led to successful drugs (HIV protease inhibitors, kinase inhibitors)
• Integrates structural biology, medicinal chemistry, and bioinformatics approaches